Integrative network biology: Graph prototyping for co-expression cancer networks

PLoS ONE

Volumn 6, Issue 7, 2011, Pages

  Kugler, Karl G ^a   Mueller, Laurin A J ^a   Graber, Armin ^a   Dehmer, Matthias ^a  

^a UMIT   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; GENE EXPRESSION; GENE MAPPING; GENE REGULATORY NETWORK; HUMAN; KNOWLEDGE; MICROARRAY ANALYSIS; PROSTATE CANCER; STATISTICAL ANALYSIS; STATISTICAL DISTRIBUTION; ALGORITHM; BIOLOGY; COMPUTER GRAPHICS; COMPUTER SIMULATION; GENETICS; MALE; METABOLISM; PROSTATE TUMOR; PROTEIN ANALYSIS;

ALGORITHMS; COMPUTATIONAL BIOLOGY; COMPUTER GRAPHICS; COMPUTER SIMULATION; GENE REGULATORY NETWORKS; HUMANS; MALE; PROSTATIC NEOPLASMS; PROTEIN INTERACTION MAPPING;

EID: 79960926869     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0022843     Document Type: Article

References (68)

1. Strohman R, (2002) Maneuvering in the complex path from genotype to phenotype. Science 296: 701-703.
2. Emmert-Streib F, Dehmer M, (2008) Analysis of Microarray Data: A Network-Based Approach. Weinheim, Germany Wiley-VCH.
3. Emmert-Streib F, Dehmer M, (2011) Networks for Systems Biology: Conceptual Connection of Data and Function. IET Syst Biol (in press).
4. Erler JT, Linding R, (2010) Network-based drugs and biomarkers. J Pathol 220: 290-296.
5. Kreeger PK, Lauffenburger DA, (2010) Cancer systems biology: a network modeling perspective. Carcinogenesis 31: 2-8.
6. Kholodenko BN, (2009) Spatially distributed cell signalling. FEBS Lett 583: 4006-4012.
7. Goble C, Stevens R, (2008) State of the nation in data integration for bioinformatics. J Biomed Inform 41: 687-693.
8. Müller-Linow M, Weckwerth W, Hütt MT, (2007) Consistency analysis of metabolic correlation networks. BMC Syst Biol 1: 44.
9. Guzmn-Vargas L, Santilln M, (2008) Comparative analysis of the transcription-factor gene regulatory networks of E. coli and S. cerevisiae. BMC Syst Biol 2: 13.
10. Cootes AP, Muggleton SH, Sternberg MJE, (2007) The identification of similarities between biological networks: application to the metabolome and interactome. J Mol Biol 369: 1126-1139.
11. Jupiter DC, VanBuren V, (2008) A visual data mining tool that facilitates reconstruction of transcription regulatory networks. PLoS One 3: e1717.
12. Sobik F, (1986) Modellierung von Vergleichsprozessen auf der Grundlage von ähnlichkeitsmaßen für Graphen. ZKI-Informationen, Akad Wiss DDR 4: 104-144.
13. Bunke H, (2000) Recent Developments in Graph Matching. pp. 2117-2124 In:Proceedings 15th International Conference on Pattern Recognition.
14. Sokolsky O, Kannan S, Lee I, (2006) Simulation-Based Graph Similarity. In: Hermanns H, Palsberg J, editors. Springer pp. 426-440 Tools and Algorithms for the Construction and Analysis of Systems, 12th International Conference, TACAS 2006 Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2006, Vienna, Austria, March 25- April 2, 2006, Proceedings volume 3920 of Lecture Notes in Computer Science.
15. Piruzian E, Bruskin S, Ishkin A, Abdeev R, Moshkovskii S, et al. (2010) Integrated network analysis of transcriptomic and proteomic data in psoriasis. BMC Syst Biol 4: 41.
16. Przulj N, (2007) Biological network comparison using graphlet degree distribution. Bioinformatics 23: e177-e183.
17. Kuchaiev O, Milenkovic T, Memisevic V, Hayes W, Przulj N, (2010) Topological network alignment uncovers biological function and phylogeny. J R Soc Interface 7: 1341-1354.
18. Emmert-Streib F, Dehmer M, Kilian J, (2005) Classification of Large Graphs by a Local Tree Decomposition. In: Arabnia HR, Scime A, editors. pp. 200-207 Proceedings of DMIN'05, International Conference on Data Mining, Las Vegas, June20-23.
19. Kugler KG, Mueller LA, Gallasch R, Graber A, Dehmer M, (2010) A novel majority vote count algorithm for integrative analysis of association networks. In: Arabnia HR, Tran QN, editors. volume 1: 62-67 International Conference on Bioinformatics & Computational Biology (BIOCOMP2010).
20. Wang K, Narayanan M, Zhong H, Tompa M, Schadt EE, et al. (2009) Meta-analysis of interspecies liver co-expression networks elucidates traits associated with common human diseases. PLoS Comput Biol 5: e1000616.
21. Dehmer M, Emmert-Streib F, Gesell T, (2008) A comparative analysis of multidimensional features of objects resembling sets of graphs. Applied Mathematics and Computation 196: 221-235.
22. Semple C, Steel M, (2003) Phylogenetics. Oxford University Press Graduate Series Mathematics and its Applications.
23. Mehler A, Gleim R, Dehmer M, (2005) Towards Structure-Sensitive Hypertext Categorization. Berlin/New York Springer, LNCS Proceedings of the 29th Annual Conference of the German Classification Society, Universität Magdeburg, March 9-11.
24. Crawford ED, (2009) Understanding the epidemiology, natural history, and key pathways involved in prostate cancer. Urology 73: S4-10.
25. Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, et al. (2009) NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res 37: D885-D890.
26. Parkinson H, Kapushesky M, Kolesnikov N, Rustici G, Shojatalab M, et al. (2009) ArrayExpress update-from an archive of functional genomics experiments to the atlas of gene expression. Nucleic Acids Res 37: D868-D872.
27. Rhodes DR, Kalyana-Sundaram S, Mahavisno V, Varambally R, Yu J, et al. (2007) Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia 9: 166-180.
28. Chandran UR, Ma C, Dhir R, Bisceglia M, Lyons-Weiler M, et al. (2007) Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process. BMC Cancer 7: 64.
29. Liu P, Ramachandran S, Seyed MA, Scharer CD, Laycock N, et al. (2006) Sex-determining region Y box 4 is a transforming oncogene in human prostate cancer cells. Cancer Res 66: 4011-4019.
30. Wallace TA, Prueitt RL, Yi M, Howe TM, Gillespie JW, et al. (2008) Tumor immunobiological differences in prostate cancer between African-American and European-American men. Cancer Res 68: 927-936.
31. Tsavachidou D, McDonnell TJ, Wen S, Wang X, Vakar-Lopez F, et al. (2009) Selenium and vitamin E: cell type- and intervention-specific tissue effects in prostate cancer. J Natl Cancer Inst 101: 306-320.
32. Singh D, Febbo PG, Ross K, Jackson DG, Manola J, et al. (2002) Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1: 203-209.
33. Yu YP, Landsittel D, Jing L, Nelson J, Ren B, et al. (2004) Gene expression alterations in prostate cancer predicting tumor aggression and preceding development of malignancy. J Clin Oncol 22: 2790-2799.
34. Wang Y, Xia XQ, Jia Z, Sawyers A, Yao H, et al. (2010) In silico estimates of tissue components in surgical samples based on expression profiling data. Cancer Res 70: 6448-6455.
35. Durinck S, Spellman PT, Birney E, Huber W, (2009) Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc 4: 1184-1191.
36. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, et al. (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5: R80.
37. Altay G, Emmert-Streib F, (2010) Inferring the conservative causal core of gene regulatory networks. BMC Syst Biol 4: 132.
38. Meyer P, Marbach D, Roy S, Kellis M, (2010) Information-Theoretic Inference of Gene Networks Using Backward Elimination. pp. 700-705 In: BIOCOMP.
39. Meyer PE, Lafitte F, Bontempi G, (2008) minet: A R/Bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinformatics 9: 461.
40. Langfelder P, Horvath S, (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9: 559.
41. Opgen-Rhein R, Strimmer K, (2007) From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Syst Biol 1: 37.
42. Ruan J, Dean AK, Zhang W, (2010) A general co-expression network-based approach to gene expression analysis: comparison and applications. BMC Syst Biol 4: 8.
43. Emmert-Streib F, Dehmer M, (2009) Hierarchical coordination of periodic genes in the cell cycle of Saccharomyces cerevisiae. BMC Syst Biol 3: 76.
44. Dehmer M, Mehler A, (2007) A new method of measuring similarity for a special class of directed graphs. Tatra Mt Math Publ 36: 39-59.
45. Bunke H, Günter S, Jiang X, (2001) Towards bridging the gap between statistical and structural pattern recognition: Two new concepts in graph matching. pp. 1-11 Advances in Pattern Recognition-ICAPR 2001.
46. Bunke H, (1983) What is the distance between graphs? Bulletin of the EATCS 20: 35-39.
47. Kullback S, Leibler RA, (1951) On information and sufficiency. The Annals of Mathematical Statistics 22: 79-86.
48. Levenshtein V, (1966) Binary Codes Capable of Correcting Deletions, Insertions and Reversals. Soviet Physics Doklady 10: 707-710.
49. Dickinson PJ, Bunke H, Dadej A, Kraetzl M, (2004) Matching graphs with unique node labels. Pattern Anal Appl 7: 243-254.
50. Emmert-Streib F, Dehmer M, (2008) Detecting Pathological Pathways of a Complex Disease. In: Emmert-Streib F, Dehmer M, editors. pp. 285-305 Analysis of Microarray Data: A Network-Based Approach, Wiley-VCH.
51. Emmert-Streib F, (2007) The chronic fatigue syndrome: a comparative pathway analysis. J Comput Biol 14: 961-972.
52. Cover TM, Thomas JA, (1991) Elements of information theory. New York, NY, USA Wiley-Interscience.
53. Wiles AM, Doderer M, Ruan J, Gu TT, Ravi D, et al. (2010) Building and analyzing protein interactome networks by cross-species comparisons. BMC Syst Biol 4: 36.
54. Michoel T, Smet RD, Joshi A, de Peer YV, Marchal K, (2009) Comparative analysis of modulebased versus direct methods for reverse-engineering transcriptional regulatory networks. BMC Syst Biol 3: 49.
55. Minnhagen P, Bernhardsson S, (2008) The blind watchmaker network: scale-freeness and evolution. PLoS One 3: e1690.
56. Dehmer M, Varmuza K, Borgert S, Emmert-Streib F, (2009) On entropy-based molecular descriptors: statistical analysis of real and synthetic chemical structures. J Chem Inf Model 49: 1655-1663.
57. Skorobogatov VA, Dobrynin AA, (1988) Metrical Analysis of Graphs. Commun Math Comp Chem 23: 105-155.
58. Mowshowitz A, (1968) Entropy and the complexity of the graphs I: An index of the relative complexity of a graph. Bull Math Biophys 30: 175-204.
59. Bonchev D, (1983) Information Theoretic Indices for Characterization of Chemical Structures. Chichester Research Studies Press.
60. Mueller LAJ, Kugler KG, Dander A, Graber A, Dehmer M, (2011) QuACN: an R package for analyzing complex biological networks quantitatively. Bioinformatics 27: 140-141.
61. Wang L, Tang H, Thayanithy V, Subramanian S, Oberg AL, et al. (2009) Gene networks and microRNAs implicated in aggressive prostate cancer. Cancer Res 69: 9490-9497.
62. Albert R, Jeong H, Barabasi AL, (2000) Error and attack tolerance of complex networks. Nature 406: 378-382.
63. Barabsi AL, Bonabeau E, (2003) Scale-free networks. Sci Am 288: 60-69.
64. Emmert-Streib F, Dehmer M, (2009) Information processing in the transcriptional regulatory network of yeast: functional robustness. BMC Syst Biol 3: 35.
65. Watts DJ, Strogatz SH, (1998) Collective dynamics of 'small-world' networks. Nature 393: 440-442.
66. Mueller LA, Kugler KG, Dander A, Graber A, Dehmer M, (2010) A Network-based Approach to Classify Disease Stages of Prostate Cancer Using Quantitative Network Measures. In: Arabnia HR, Tran QN, editors. volume 1: 55-61 International Conference on Bioinformatics & Computational Biology (BIOCOMP2010).
67. Képès F, (2008) Biological Networks (Complex Systems and Interdisciplinary Science), World Scientific Publishing, chapter Transcriptional Networks pp. 83-131.
68. Schramm G, Kannabiran N, König R, (2010) Regulation patterns in signaling networks of cancer. BMC Syst Biol 4: 162.